Chapter 8 HMSC analysis
8.2 Variance partitioning
# Compute variance partitioning
varpart=computeVariancePartitioning(m)
varpart$vals %>%
as.data.frame() %>%
rownames_to_column(var="variable") %>%
pivot_longer(!variable, names_to = "genome", values_to = "value") %>%
mutate(variable=factor(variable, levels=rev(c("origin","sex","logseqdepth","Random: location")))) %>%
group_by(variable) %>%
summarise(mean=mean(value)*100,sd=sd(value)*100) %>%
tt()| variable | mean | sd |
|---|---|---|
| Random: location | 37.900015 | 25.317903 |
| logseqdepth | 56.110626 | 25.796874 |
| sex | 4.937460 | 5.612719 |
| origin | 1.051899 | 1.282563 |
# Basal tree
varpart_tree <- genome_tree
#Varpart table
varpart_table <- varpart$vals %>%
as.data.frame() %>%
rownames_to_column(var="variable") %>%
pivot_longer(!variable, names_to = "genome", values_to = "value") %>%
mutate(genome=factor(genome, levels=rev(varpart_tree$tip.label))) %>%
mutate(variable=factor(variable, levels=rev(c("origin","sex","logseqdepth","Random: location"))))
#Phylums
phylum_colors <- read_tsv("https://raw.githubusercontent.com/earthhologenome/EHI_taxonomy_colour/main/ehi_phylum_colors.tsv") %>%
mutate(phylum=str_remove_all(phylum, "p__"))%>%
right_join(genome_metadata, by=join_by(phylum == phylum)) %>%
filter(genome %in% varpart_tree$tip.label) %>%
arrange(match(genome, varpart_tree$tip.label)) %>%
mutate(phylum = factor(phylum, levels = unique(phylum))) %>%
column_to_rownames(var = "genome") %>%
select(phylum)
colors_alphabetic <- read_tsv("https://raw.githubusercontent.com/earthhologenome/EHI_taxonomy_colour/main/ehi_phylum_colors.tsv") %>%
mutate(phylum=str_remove_all(phylum, "p__"))%>%
right_join(genome_metadata, by=join_by(phylum == phylum)) %>%
filter(genome %in% varpart_tree$tip.label) %>%
arrange(match(genome, varpart_tree$tip.label)) %>%
select(phylum, colors) %>%
unique() %>%
arrange(phylum) %>%
select(colors) %>%
pull()
# Basal ggtree
varpart_tree <- varpart_tree %>%
force.ultrametric(.,method="extend") %>%
ggtree(., size = 0.3)***************************************************************
* Note: *
* force.ultrametric does not include a formal method to *
* ultrametricize a tree & should only be used to coerce *
* a phylogeny that fails is.ultrametric due to rounding -- *
* not as a substitute for formal rate-smoothing methods. *
***************************************************************
# Add phylum colors next to the tree tips
varpart_tree <- gheatmap(varpart_tree, phylum_colors, offset=-0.2, width=0.1, colnames=FALSE) +
scale_fill_manual(values=colors_alphabetic)+
labs(fill="Phylum")
#Reset fill scale to use a different colour profile in the heatmap
varpart_tree <- varpart_tree + new_scale_fill()
# Add variance stacked barplot
vertical_tree <- varpart_tree +
scale_fill_manual(values=c("#506a96","#cccccc","#be3e2b","#f6de6c"))+
geom_fruit(
data=varpart_table,
geom=geom_bar,
mapping = aes(x=value, y=genome, fill=variable, group=variable),
pwidth = 2,
offset = 0.05,
width= 1,
orientation="y",
stat="identity")+
labs(fill="Variable")
vertical_tree8.3 Posterior estimates
# Select desired support threshold
support=0.9
negsupport=1-support
# Basal tree
postestimates_tree <- genome_tree
# Posterior estimate table
post_beta <- getPostEstimate(hM=m, parName="Beta")$support %>%
as.data.frame() %>%
mutate(variable=m$covNames) %>%
pivot_longer(!variable, names_to = "genome", values_to = "value") %>%
mutate(genome=factor(genome, levels=rev(postestimates_tree$tip.label))) %>%
mutate(value = case_when(
value >= support ~ "Positive",
value <= negsupport ~ "Negative",
TRUE ~ "Neutral")) %>%
mutate(value=factor(value, levels=c("Positive","Neutral","Negative"))) %>%
pivot_wider(names_from = variable, values_from = value) %>%
#select(genome,sp_vulgaris,area_semi,area_urban,sp_vulgarisxarea_semi,sp_vulgarisxarea_urban,season_spring,season_winter,sp_vulgarisxseason_spring,sp_vulgarisxseason_winter) %>%
column_to_rownames(var="genome")
#Phylums
phylum_colors <- read_tsv("https://raw.githubusercontent.com/earthhologenome/EHI_taxonomy_colour/main/ehi_phylum_colors.tsv") %>%
mutate(phylum=str_remove_all(phylum, "p__")) %>%
right_join(genome_metadata, by=join_by(phylum == phylum)) %>%
filter(genome %in% postestimates_tree$tip.label) %>%
arrange(match(genome, postestimates_tree$tip.label)) %>%
mutate(phylum = factor(phylum, levels = unique(phylum))) %>%
column_to_rownames(var = "genome") %>%
select(phylum)
colors_alphabetic <- read_tsv("https://raw.githubusercontent.com/earthhologenome/EHI_taxonomy_colour/main/ehi_phylum_colors.tsv") %>%
mutate(phylum=str_remove_all(phylum, "p__")) %>%
right_join(genome_metadata, by=join_by(phylum == phylum)) %>%
filter(genome %in% postestimates_tree$tip.label) %>%
arrange(match(genome, postestimates_tree$tip.label)) %>%
select(phylum, colors) %>%
unique() %>%
arrange(phylum) %>%
select(colors) %>%
pull()
# Basal ggtree
postestimates_tree <- postestimates_tree %>%
force.ultrametric(.,method="extend") %>%
ggtree(., size = 0.3)***************************************************************
* Note: *
* force.ultrametric does not include a formal method to *
* ultrametricize a tree & should only be used to coerce *
* a phylogeny that fails is.ultrametric due to rounding -- *
* not as a substitute for formal rate-smoothing methods. *
***************************************************************
#Add phylum colors next to the tree tips
postestimates_tree <- gheatmap(postestimates_tree, phylum_colors, offset=-0.2, width=0.1, colnames=FALSE) +
scale_fill_manual(values=colors_alphabetic)+
labs(fill="Phylum")
#Reset fill scale to use a different colour profile in the heatmap
postestimates_tree <- postestimates_tree + new_scale_fill()
# Add posterior significant heatmap
postestimates_tree <- gheatmap(postestimates_tree, post_beta, offset=0, width=0.5, colnames=TRUE, colnames_position="top",colnames_angle=90, colnames_offset_y=1, hjust=0) +
scale_fill_manual(values=c("#be3e2b","#f4f4f4","#b2b530"))+
labs(fill="Trend")
postestimates_tree +
vexpand(.25, 1) # expand top 8.4 Correlations
#Compute the residual correlation matrix
OmegaCor = computeAssociations(m)
# Refernece tree (for sorting genomes)
genome_tree_subset <- genome_tree %>%
keep.tip(., tip=m$spNames)
#Co-occurrence matrix at the animal level
supportLevel = 0.95
toPlot = ((OmegaCor[[1]]$support>supportLevel)
+ (OmegaCor[[1]]$support<(1-supportLevel))>0)*OmegaCor[[1]]$mean
matrix <- toPlot %>%
as.data.frame() %>%
rownames_to_column(var="genome1") %>%
pivot_longer(!genome1, names_to = "genome2", values_to = "cor") %>%
mutate(genome1= factor(genome1, levels=genome_tree_subset$tip.label)) %>%
mutate(genome2= factor(genome2, levels=genome_tree_subset$tip.label)) %>%
ggplot(aes(x = genome1, y = genome2, fill = cor)) +
geom_tile() +
scale_fill_gradient2(low = "#be3e2b",
mid = "#f4f4f4",
high = "#b2b530")+
theme_void()
htree <- genome_tree_subset %>%
force.ultrametric(.,method="extend") %>%
ggtree(.)***************************************************************
* Note: *
* force.ultrametric does not include a formal method to *
* ultrametricize a tree & should only be used to coerce *
* a phylogeny that fails is.ultrametric due to rounding -- *
* not as a substitute for formal rate-smoothing methods. *
***************************************************************
***************************************************************
* Note: *
* force.ultrametric does not include a formal method to *
* ultrametricize a tree & should only be used to coerce *
* a phylogeny that fails is.ultrametric due to rounding -- *
* not as a substitute for formal rate-smoothing methods. *
***************************************************************
#create composite figure
grid.arrange(grobs = list(matrix,vtree),
layout_matrix = rbind(c(2,1,1,1,1,1,1,1,1,1,1,1),
c(2,1,1,1,1,1,1,1,1,1,1,1),
c(2,1,1,1,1,1,1,1,1,1,1,1),
c(2,1,1,1,1,1,1,1,1,1,1,1),
c(2,1,1,1,1,1,1,1,1,1,1,1),
c(2,1,1,1,1,1,1,1,1,1,1,1),
c(2,1,1,1,1,1,1,1,1,1,1,1),
c(2,1,1,1,1,1,1,1,1,1,1,1),
c(2,1,1,1,1,1,1,1,1,1,1,1),
c(2,1,1,1,1,1,1,1,1,1,1,1),
c(2,1,1,1,1,1,1,1,1,1,1,1)))8.5 Predict responses
# Select modelchain of interest
load("hmsc/fit_model1_250_10.Rdata")
gradient = c("domestic","feral")
gradientlength = length(gradient)
#Treatment-specific gradient predictions
pred <- constructGradient(m,
focalVariable = "origin",
non.focalVariables = list(logseqdepth=list(1),location=list(1))) %>%
predict(m, Gradient = ., expected = TRUE) %>%
do.call(rbind,.) %>%
as.data.frame() %>%
mutate(origin=rep(gradient,1000)) %>%
pivot_longer(!origin,names_to = "genome", values_to = "value")# weights: 9 (4 variable)
initial value 101.072331
final value 91.392443
converged
8.5.0.1 Element level
elements_table <- genome_gifts %>%
to.elements(., GIFT_db) %>%
as.data.frame()
community_elements <- pred %>%
group_by(origin, genome) %>%
mutate(row_id = row_number()) %>%
pivot_wider(names_from = genome, values_from = value) %>%
ungroup() %>%
group_split(row_id) %>%
as.list() %>%
lapply(., FUN = function(x){x %>%
select(-row_id) %>%
column_to_rownames(var = "origin") %>%
as.data.frame() %>%
exp() %>%
t() %>%
tss() %>%
to.community(elements_table,.,GIFT_db) %>%
as.data.frame() %>%
rownames_to_column(var="origin")
})
calculate_slope <- function(x) {
lm_fit <- lm(unlist(x) ~ seq_along(unlist(x)))
coef(lm_fit)[2]
}
element_predictions <- map_dfc(community_elements, function(mat) {
mat %>%
column_to_rownames(var = "origin") %>%
t() %>%
as.data.frame() %>%
rowwise() %>%
mutate(slope = calculate_slope(c_across(everything()))) %>%
select(slope) }) %>%
t() %>%
as.data.frame() %>%
set_names(colnames(community_elements[[1]])[-1]) %>%
rownames_to_column(var="iteration") %>%
pivot_longer(!iteration, names_to="trait",values_to="value") %>%
group_by(trait) %>%
summarise(mean=mean(value),
p1 = quantile(value, probs = 0.1),
p9 = quantile(value, probs = 0.9),
positive_support = sum(value > 0)/1000,
negative_support = sum(value < 0)/1000) %>%
arrange(-positive_support)# Positively associated
element_predictions %>%
filter(mean >0) %>%
arrange(-positive_support) %>%
filter(positive_support>=0.9) %>%
tt()| trait | mean | p1 | p9 | positive_support | negative_support |
|---|---|---|---|---|---|
| D0205 | 0.012598000 | 0.0023490224 | 0.023531763 | 0.948 | 0.052 |
| D0906 | 0.003856608 | 0.0001844596 | 0.008372241 | 0.931 | 0.069 |
| D0208 | 0.009860536 | 0.0017838550 | 0.017932200 | 0.922 | 0.078 |
| D0504 | 0.004658815 | 0.0002366114 | 0.009902129 | 0.908 | 0.092 |
| D0507 | 0.003955784 | 0.0001165261 | 0.007343063 | 0.908 | 0.092 |
| B0103 | 0.008498845 | 0.0001308317 | 0.017178267 | 0.906 | 0.094 |
element_predictions %>%
filter(mean <0) %>%
arrange(-negative_support) %>%
filter(negative_support>=0.9) %>%
tt()| trait | mean | p1 | p9 | positive_support | negative_support |
|---|---|---|---|---|---|
| D0801 | -0.001652917 | -0.002151535 | -1.048160e-04 | 0.005 | 0.995 |
| D0802 | -0.001652917 | -0.002151535 | -1.048160e-04 | 0.005 | 0.995 |
| D0517 | -0.004601572 | -0.007857518 | -1.214754e-03 | 0.030 | 0.970 |
| B0709 | -0.002137926 | -0.003711645 | -5.770036e-04 | 0.035 | 0.965 |
| B0302 | -0.004889616 | -0.010638724 | -5.415953e-04 | 0.036 | 0.964 |
| D0611 | -0.004076752 | -0.009381879 | -2.137319e-04 | 0.042 | 0.958 |
| D0903 | -0.004076752 | -0.009381879 | -2.137319e-04 | 0.042 | 0.958 |
| B0219 | -0.004102791 | -0.009531123 | -2.138679e-04 | 0.043 | 0.957 |
| D0601 | -0.009419047 | -0.017870105 | -2.354803e-03 | 0.044 | 0.956 |
| B0310 | -0.012666099 | -0.023473519 | -2.643906e-03 | 0.046 | 0.954 |
| D0817 | -0.004961877 | -0.010745331 | -4.754330e-04 | 0.050 | 0.950 |
| D0603 | -0.001962825 | -0.003870224 | -3.352175e-04 | 0.052 | 0.948 |
| D0807 | -0.004206442 | -0.008820872 | -5.491684e-04 | 0.056 | 0.944 |
| D0610 | -0.003082764 | -0.005046227 | -8.629378e-04 | 0.057 | 0.943 |
| B0804 | -0.016028324 | -0.029638644 | -3.465492e-03 | 0.058 | 0.942 |
| B0303 | -0.011417557 | -0.021341370 | -1.732196e-03 | 0.070 | 0.930 |
| B0603 | -0.016022751 | -0.032242536 | -1.962945e-03 | 0.070 | 0.930 |
| D0908 | -0.015809551 | -0.028824706 | -2.833161e-03 | 0.071 | 0.929 |
| B0214 | -0.021197078 | -0.039247507 | -3.021418e-03 | 0.072 | 0.928 |
| D0606 | -0.005747534 | -0.011465796 | -5.709914e-04 | 0.074 | 0.926 |
| D0508 | -0.003289659 | -0.007703875 | -7.819120e-05 | 0.083 | 0.917 |
| B0601 | -0.008981230 | -0.018091207 | -6.178199e-04 | 0.086 | 0.914 |
| B0401 | -0.011562378 | -0.022822641 | -5.148959e-04 | 0.087 | 0.913 |
| D0612 | -0.001698286 | -0.002927157 | -9.510122e-05 | 0.087 | 0.913 |
| B0309 | -0.007734646 | -0.015600834 | -8.281195e-05 | 0.095 | 0.905 |
| D0816 | -0.005808299 | -0.012158922 | -2.032688e-04 | 0.096 | 0.904 |
| B0204 | -0.015135407 | -0.032062082 | -2.853902e-04 | 0.097 | 0.903 |
positive <- element_predictions %>%
filter(mean >0) %>%
arrange(mean) %>%
filter(positive_support>=0.9) %>%
select(-negative_support) %>%
rename(support=positive_support)
negative <- element_predictions %>%
filter(mean <0) %>%
arrange(mean) %>%
filter(negative_support>=0.9) %>%
select(-positive_support) %>%
rename(support=negative_support)
bind_rows(positive,negative) %>%
left_join(GIFT_db,by=join_by(trait==Code_element)) %>%
mutate(trait=factor(trait,levels=c(rev(positive$trait),rev(negative$trait)))) %>%
ggplot(aes(x=mean, y=fct_rev(trait), xmin=p1, xmax=p9, color=Function)) +
geom_point() +
geom_errorbar() +
xlim(c(-0.04,0.04)) +
geom_vline(xintercept=0) +
scale_color_manual(values = c("#debc14","#440526","#dc7c17","#172742","#debc14","#440526","#dc7c17","#172742","#357379","#6c7e2c","#d8dc69","#774d35","#db717d")) +
theme_minimal() +
labs(x="Regression coefficient",y="Functional trait")8.5.0.2 Function level
functions_table <- elements_table %>%
to.functions(., GIFT_db) %>%
as.data.frame()
community_functions <- pred %>%
group_by(origin, genome) %>%
mutate(row_id = row_number()) %>%
pivot_wider(names_from = genome, values_from = value) %>%
ungroup() %>%
group_split(row_id) %>%
as.list() %>%
lapply(., FUN = function(x){x %>%
select(-row_id) %>%
column_to_rownames(var = "origin") %>%
as.data.frame() %>%
exp() %>%
t() %>%
tss() %>%
to.community(functions_table,.,GIFT_db) %>%
as.data.frame() %>%
rownames_to_column(var="origin")
})#max-min option
calculate_slope <- function(x) {
lm_fit <- lm(unlist(x) ~ seq_along(unlist(x)))
coef(lm_fit)[2]
}
function_predictions <- map_dfc(community_functions, function(mat) {
mat %>%
column_to_rownames(var = "origin") %>%
t() %>%
as.data.frame() %>%
rowwise() %>%
mutate(slope = calculate_slope(c_across(everything()))) %>%
select(slope) }) %>%
t() %>%
as.data.frame() %>%
set_names(colnames(community_functions[[1]])[-1]) %>%
rownames_to_column(var="iteration") %>%
pivot_longer(!iteration, names_to="trait",values_to="value") %>%
group_by(trait) %>%
summarise(mean=mean(value),
p1 = quantile(value, probs = 0.1),
p9 = quantile(value, probs = 0.9),
positive_support = sum(value > 0)/1000,
negative_support = sum(value < 0)/1000) %>%
arrange(-positive_support)
# Positively associated
function_predictions %>%
filter(mean >0) %>%
arrange(-positive_support) %>%
tt()| trait | mean | p1 | p9 | positive_support | negative_support |
|---|---|---|---|---|---|
| D02 | 8.297381e-03 | -0.0034767283 | 0.0207677742 | 0.811 | 0.189 |
| B08 | 7.845217e-03 | -0.0031696928 | 0.0176622281 | 0.789 | 0.211 |
| B01 | 1.081773e-03 | -0.0065012399 | 0.0081741420 | 0.616 | 0.384 |
| S01 | 8.592019e-04 | -0.0130338151 | 0.0137154306 | 0.573 | 0.427 |
| B10 | 2.004382e-06 | -0.0002987702 | 0.0002626824 | 0.497 | 0.503 |
| B09 | 3.708401e-05 | -0.0005547998 | 0.0005152844 | 0.367 | 0.633 |
# Negatively associated
function_predictions %>%
filter(mean <0) %>%
arrange(-negative_support) %>%
tt()| trait | mean | p1 | p9 | positive_support | negative_support |
|---|---|---|---|---|---|
| D08 | -0.0011505608 | -0.002205838 | -0.0001933344 | 0.043 | 0.957 |
| B03 | -0.0105800233 | -0.018027872 | -0.0024996566 | 0.061 | 0.939 |
| D06 | -0.0031587249 | -0.006985716 | 0.0001070787 | 0.114 | 0.886 |
| B04 | -0.0080202588 | -0.017941396 | 0.0014229755 | 0.152 | 0.848 |
| D07 | -0.0121782070 | -0.028597144 | 0.0039737568 | 0.175 | 0.825 |
| B06 | -0.0066092580 | -0.016952017 | 0.0030489265 | 0.189 | 0.811 |
| D05 | -0.0015695446 | -0.007413673 | 0.0041523303 | 0.218 | 0.782 |
| D03 | -0.0041713761 | -0.012777500 | 0.0033036602 | 0.230 | 0.770 |
| S03 | -0.0092505734 | -0.031613460 | 0.0170041249 | 0.256 | 0.744 |
| B02 | -0.0031647641 | -0.012342889 | 0.0051270290 | 0.284 | 0.716 |
| D09 | -0.0017619642 | -0.007887604 | 0.0053050996 | 0.301 | 0.699 |
| S02 | -0.0043281804 | -0.014879874 | 0.0035011445 | 0.343 | 0.657 |
| B07 | -0.0034669779 | -0.015505790 | 0.0087514205 | 0.344 | 0.656 |
| D01 | -0.0001653947 | -0.005040160 | 0.0048526834 | 0.435 | 0.565 |
positive <- function_predictions %>%
filter(mean >0) %>%
arrange(mean) %>%
filter(positive_support>=0.9) %>%
select(-negative_support) %>%
rename(support=positive_support)
negative <- function_predictions %>%
filter(mean <0) %>%
arrange(mean) %>%
filter(negative_support>=0.9) %>%
select(-positive_support) %>%
rename(support=negative_support)
bind_rows(positive,negative) %>%
left_join(GIFT_db,by=join_by(trait==Code_function)) %>%
mutate(trait=factor(trait,levels=c(rev(positive$trait),rev(negative$trait)))) %>%
ggplot(aes(x=mean, y=fct_rev(trait), xmin=p1, xmax=p9, color=Function)) +
geom_point() +
geom_errorbar() +
xlim(c(-0.02,0.02)) +
geom_vline(xintercept=0) +
scale_color_manual(values = c("#debc14","#440526","#dc7c17","#172742","#debc14","#440526","#dc7c17","#172742","#357379","#6c7e2c","#d8dc69","#774d35","#db717d")) +
theme_minimal() +
labs(x="Regression coefficient",y="Functional trait")